S^(hJ!dZddlZddlZddlZddlZddlmZmZmZm Z ddl Z ddl Z ddl m Z ddl mZddlmZddlmZdd lmZmZdd lmZmZmZmZmZdd lmZdd lmZm Z m!Z!m"Z"m#Z#d dl$m%Z%m&Z&ddl'm(Z(e"jRe*Z+dZ,dZ-de j\de/de/fdZ0Gdde jbZ2Gdde jbZ3Gdde jhZ5de5iZ6Gdde jhZ7Gd d!e jhZ8Gd"d#eZ9d$Z:d%Z;d&Z<Gd'd(e9Z=Gd)d*e9Z>e d+e:Gd,d-e9Z?e d.e:Gd/d0e9eZ@Gd1d2e9ZAGd3d4e9eZBgd5ZCy)6zPyTorch Blenderbot model.N)ListOptionalTupleUnion)nn)CrossEntropyLoss)ACT2FN)GenerationMixin)_prepare_4d_attention_mask!_prepare_4d_causal_attention_mask)BaseModelOutput)BaseModelOutputWithPastAndCrossAttentions!CausalLMOutputWithCrossAttentionsSeq2SeqLMOutputSeq2SeqModelOutput)PreTrainedModel)add_end_docstringsadd_start_docstrings%add_start_docstrings_to_model_forwardloggingreplace_return_docstrings)'BlenderbotSmallForConditionalGenerationBlenderbotSmallModel)BlenderbotConfigrz facebook/blenderbot-400M-distill input_ids pad_token_iddecoder_start_token_idc|j|j}|ddddfj|ddddf<||dddf<| td|j |dk(||S)z1 Shift input ids one token to the right. Nrrz1self.model.config.pad_token_id has to be defined.i) new_zerosshapeclone ValueError masked_fill_)rrr shifted_input_idss p/var/www/skyplay_api_hub/venv/lib/python3.12/site-packages/transformers/models/blenderbot/modeling_blenderbot.pyshift_tokens_rightr*9s}"++IOO<(CRC0668ae4adLMM""#4#.D.DQ.GG(*J)!,L T[[1A%BBLJ;;t{{3C'Db#NL  'T[[%?SIJ;;t{{='A2sKLN1$5z#BJJ 99nQ&7%FANLT[[%?SIJ;;t{{='A2sKL ??),7NDNN*B > Ct{{<#>CCZP 'Z''4 +|++Z8 //!$yyz/C/CAq/IJ    3#7'"J J6dnn8LgW^7_6`a %%'(*   %""$a'(BB 7a'8R7SS\]k]p]p]r\st(,,S$..'7SVddL',,S4>>-A7GTL}},,\r,B  &##%$..):: Et~~FWEXY',,./1+//2q!<|?P?PQTVZVdVdfmov?wwL',,S4>>-A7GTL  %1$5$5c4>>7T[$\ !055cDNN6JGU\]L$( !]]**<4<AAr+)FTFN)NNNNF)rCrDrErFrGrRboolrrr3r;rSrkrr?rIrJs@r)rUrUisNG  -1CCC C  C  CC)*C>eU\\eCece 488<1526"'vB||vB#5<<0vB!u||!45 vB !. vB "%,,/ vB vB u||Xell3XeELL>Q5RR SvBr+rUeagerc eZdZdeffd Z d dej dej dej dedej f dZxZ S) BlenderbotEncoderLayerr\ct||j|_t |j |j|j |j||_tj|j|_ |j|_ t|j|_|j |_tj"|j|j$|_tj"|j$|j|_tj|j|_y)N)rVrWrXr\)r2r3d_modelrVBLENDERBOT_ATTENTION_CLASSES_attn_implementationencoder_attention_headsattention_dropout self_attnr LayerNormself_attn_layer_normrXr activation_function activation_fnactivation_dropoutraencoder_ffn_dimfc1fc2final_layer_normr4r\r5s r)r3zBlenderbotEncoderLayer.__init__ s 5f6Q6QRnn44,,   %'LL$@!~~ #F$>$>?"(";";99T^^V-C-CD99V33T^^D " T^^ <r+rlrorprqrrc|}|j|}|j||||\}}}tjj ||j|j }||z}|}|j |}|j|j|}tjj ||j|j }|j|}tjj ||j|j }||z}|jtjk(rEtj|jjdz }tj || |}|f} |r| |fz } | S)a Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (`torch.FloatTensor`): attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size `(encoder_attention_heads,)`. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. )rlrorprqrvi)minmax)rrrr}rXrxrrrrrr9r;float16finforclamp) r4rlrorprqresidualrr clamp_valueoutputss r)r?zBlenderbotEncoderLayer.forwardsf$!11-@ )-')+/ *8* & |Q  --mt||VZVcVc-d  =0  --m< **488M+BC  --mt?V?Vaeanan-o /  --mt||VZVcVc-d  =0   %-- /++m&9&9:>>EK!KK K<[YM "    &Gr+)F) rCrDrErr3r;rSrr?rIrJs@r)rr sZ=/=.#( .||. . .  .  .r+rc<eZdZdeffd Z ddej deej deej deej deej deej d eeej d ee d ee d ej fd Z xZ S)BlenderbotDecoderLayerr\cNt||j|_t |j |j|j |jdd||_|j|_ t|j|_ |j|_ tj|j|_t |j |j|j |jd||_tj|j|_tj&|j|j(|_tj&|j(|j|_tj|j|_y)NT)rVrWrXrYr[r\)rXrYr\)r2r3rrVrrdecoder_attention_headsrrrXr rrrrrr encoder_attnencoder_attn_layer_normradecoder_ffn_dimrrrrs r)r3zBlenderbotDecoderLayer.__init__Qs0 5f6Q6QRnn44,,  ~~ #F$>$>?"(";";$&LL$@!89T9TU NN  * *,,  (*||DNN'C$99T^^V-C-CD99V33T^^D " T^^ <r+rlroencoder_hidden_statesencoder_attention_maskrpcross_attn_layer_head_maskrnrq use_cacherrc t|} |j|}||ddnd} |j|| |||\}} } tjj ||j|j }| |z}d}d}|w|} |j |}||ddnd}|j||||||\}}}tjj ||j|j }| |z}| |z} |} |j|}|j|j|}tjj ||j|j }|j|}tjj ||j|j }| |z}|f}|r|| |fz }| r|| fz }|S)a Args: hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` attention_mask (`torch.FloatTensor`): attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. encoder_hidden_states (`torch.FloatTensor`): cross attention input to the layer of shape `(batch, seq_len, embed_dim)` encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values. layer_head_mask (`torch.FloatTensor`): mask for attention heads in a given layer of size `(encoder_attention_heads,)`. cross_attn_layer_head_mask (`torch.FloatTensor`): mask for cross-attention heads in a given layer of size `(decoder_attention_heads,)`. past_key_value (`Tuple(torch.FloatTensor)`): cached past key and value projection states output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. Nr)rlrnrorprqrv)rlrmrorprnrq) rrrr}rXrxrrrrrrr)r4rlrorrrprrnrqrrself_attn_past_key_valueself_attn_weightspresent_key_valuecross_attn_present_key_valuecross_attn_weightscross_attn_past_key_valuers r)r?zBlenderbotDecoderLayer.forwardns <!11-@ :H9S>"1#5Y] >Bnn'3)+/ ?M? ; (*; --mt||VZVcVc-d  =0 (,$! ,$H 88GM@N?Yrs(;_c %NRN_N_+!65 :8"3 O`O KM-/KMM11-4< >G  )+ +Gr+)NNNNNNFT) rCrDrErr3r;rSrrrr?rIrJs@r)rrPs=/=@268<9=26=A8<,1$(W||W!.W ( 5 W !) 6 W "%,,/ W%-U\\$:W!u||!45W$D>WD>W Wr+rc.eZdZeZdZdZdZedZ y)BlenderbotPreTrainedModelmodelTc|jj}t|tjrY|j j jd||j%|jj jyyt|tjrf|j j jd||j2|j j |jjyyy)Nr)meanstd) r\init_std isinstancerrar>datanormal_rZzero_ EmbeddingrM)r4modulers r) _init_weightsz'BlenderbotPreTrainedModel._init_weightsskk"" fbii ( MM   & &CS & 9{{&   &&('  - MM   & &CS & 9!!- ""6#5#56<<>..r+c|jj}tjgddddd|gg|j}|j |||d}|S)N)r rr rr:)rordecoder_input_ids)r\rr;rfr:ne)r4 pad_tokenr dummy_inputss r)rz&BlenderbotPreTrainedModel.dummy_inputssZKK,, LL"2Q2q)4L!MVZVaVab 'll95"!* r+N) rCrDrEr config_classbase_model_prefixsupports_gradient_checkpointingrpropertyrr+r)rrs,#L&*# ?r+raO This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.) This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`BlenderbotConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. a Conversation example: ```python >>> from transformers import AutoTokenizer, BlenderbotForConditionalGeneration >>> mname = "facebook/blenderbot-400M-distill" >>> model = BlenderbotForConditionalGeneration.from_pretrained(mname) >>> tokenizer = AutoTokenizer.from_pretrained(mname) >>> UTTERANCE = "My friends are cool but they eat too many carbs." >>> print("Human: ", UTTERANCE) Human: My friends are cool but they eat too many carbs. >>> inputs = tokenizer([UTTERANCE], return_tensors="pt") >>> reply_ids = model.generate(**inputs) >>> print("Bot: ", tokenizer.batch_decode(reply_ids, skip_special_tokens=True)[0]) Bot: That's unfortunate. Are they trying to lose weight or are they just trying to be healthier? >>> REPLY = "I'm not sure" >>> print("Human: ", REPLY) Human: I'm not sure >>> NEXT_UTTERANCE = ( ... "My friends are cool but they eat too many carbs. That's unfortunate. " ... "Are they trying to lose weight or are they just trying to be healthier? " ... " I'm not sure." ... ) >>> inputs = tokenizer([NEXT_UTTERANCE], return_tensors="pt") >>> next_reply_ids = model.generate(**inputs) >>> print("Bot: ", tokenizer.batch_decode(next_reply_ids, skip_special_tokens=True)[0]) Bot: I see. Well, it's good that they're trying to change their eating habits. ``` al Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): Indices of decoder input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are decoder input IDs?](../glossary#decoder-input-ids) Blenderbot uses the `bos_token_id` as the starting token for `decoder_input_ids` generation. If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see `past_key_values`). decoder_attention_mask (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*): Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also be used by default. head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*): Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. decoder_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*): Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`) `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be input (see `past_key_values`). This is useful if you want more control over how to convert `decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix. If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value of `inputs_embeds`. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. c^eZdZdZddedeejffd Z ddZ xZ S)BlenderbotEncoderz Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a [`BlenderbotEncoderLayer`]. Args: config: BlenderbotConfig embed_tokens (nn.Embedding): output embedding r\ embed_tokensct|||j|_|j|_|j }|j |_|j|_ |jrtj|nd}|||_ n(t|j||j||_ t!|j||_t%j&t)|j*Dcgc] }t-|c}|_t%j0|j |_d|_|j7ycc}wNrQrNF)r2r3rXencoder_layerdrop layerdroprrrMmax_position_embeddingsmax_source_positionsscale_embeddingmathsqrtrrL vocab_sizer-embed_positionsr ModuleListrangeencoder_layersrlayersr layer_normgradient_checkpointing post_init)r4r\rrVrNrr5s r)r3zBlenderbotEncoder.__init__{s   ~~ 11NN !..$*$B$B!.4.D.Ddii *#  # ,D  =!!9d.>.>K!D  D  * *  mmUSYShShMi$j%;F%C$jk ,,v~~6&+#  %ks7Ec8||n|jj}||n|jj}||n|jj}| | t d|8|j |||j }|jd|d}n!||j dd}n t d||j|}|j|} || z} tjj| |j|j} |t||j}|rdnd} |rdnd} |_|j dt!|j"k7r6t dt!|j"d |j dd t%|j"D]\} }|r| | fz} d }|jr&t'j(g}||j*krd }|rd }nW|j,r3|jr'|j/|j0| |||| nd|}n|| |||| nd|}|d} |s| |dfz} |j3| } |r| | fz} |st5d| | | fDSt7| | | S)a~ Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*): Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. NzDYou cannot specify both input_ids and inputs_embeds at the same timer"z5You have to specify either input_ids or inputs_embedsrvrrz&The head_mask should be specified for  layers, but it is for .FT)NN)rprqrc3&K|] }|| ywr1r.0vs r) z,BlenderbotEncoder.forward..seqWXWdeslast_hidden_staterl attentions)r\rqoutput_hidden_statesuse_return_dictr&%warn_if_padding_and_no_attention_maskryrhrrrr}rXrxr r9lenr enumerater;randrr_gradient_checkpointing_func__call__rtupler)r4rro head_mask inputs_embedsrqr return_dict input_shape embed_posrlencoder_statesall_attentionsidx encoder_layerto_dropdropout_probability layer_outputss r)r?zBlenderbotEncoder.forwards\2C1N-TXT_T_TqTq$8$D $++JjJj &1%cd d  "  6 6y. Q#..*K!r;r?;I  &',,.s3KTU U   --i8M((5 % 1  --mt||VZVcVc-d   %7 H[H[\N30d  ~~"c$++&66 >Ne]NN$See e+>Vd  r+r1)NNNNNNN) rCrDrErFrrrrr3r?rIrJs@r)rrqsB/x ?U>!C r+rcteZdZdZddedeejffd ZdZ dZ d dZ xZ S) BlenderbotDecoderz Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`BlenderbotDecoderLayer`] Args: config: BlenderbotConfig embed_tokens (nn.Embedding): output embedding r\rct|||j|_|j|_|j |_|j|_|jrtj|jnd}|||_ n2t|j|j|j ||_ t!|j|j|_t%j&t)|j*Dcgc] }t-|c}|_t%j0|j|_d|_|j7ycc}wr)r2r3rXdecoder_layerdroprrrMrmax_target_positionsrrrrrrLrr-rrrrdecoder_layersrrrrrr)r4r\rrNrr5s r)r3zBlenderbotDecoder.__init__'s  ~~ 11!..$*$B$B!393I3Idii/s  # ,D  =!!6>>43C3CQ\!D  D  * * NN mmUSYShShMi$j%;F%C$jk ,,v~~6&+#  %ks E#c|jSr1rr4s r)get_input_embeddingsz&BlenderbotDecoder.get_input_embeddingsAs   r+c||_yr1rr4values r)set_input_embeddingsz&BlenderbotDecoder.set_input_embeddingsDs !r+c | | n|jj} | | n|jj} | | n|jj} | | n|jj} | | t d|&|j } |jd| d}n!||j dd} n t d||ddjdnd}||j|}t|| ||}||t||j| d}|j| |}||z}tjj!||j |j"}|j$r%|j"r| rt&j)d d } | rd nd}| rd nd}| r|d nd}| rd nd}t+||gd d gD]j\}}| |j dt-|j.k7s3t d|dt-|j.d|j ddt1|j.D]\}}| r||fz }|j"r%t3j4g}||j6kr?|||nd}|j$r?|j"r3|j9|j:|||||||nd|||ndd| | }n||||||||nd|||nd|| |  }|d}| r ||| rdndfz }| s||dfz }|||dfz }|j=|}| r||fz }| r|nd}| st?d|||||fDStA|||||S)a* Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*): Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) head_mask (`torch.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*): Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the cross-attention modules in the decoder to avoid performing cross-attention on hidden heads. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. NzTYou cannot specify both decoder_input_ids and decoder_inputs_embeds at the same timer"zEYou have to specify either decoder_input_ids or decoder_inputs_embedsrr)rrvzZ`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...Frrcross_attn_head_maskzThe `z` should be specified for rr)rorrrprrnrqrr rc3$K|]}|| ywr1rrs r)rz,BlenderbotDecoder.forward..s=s)rpast_key_valuesrlrcross_attentions)!r\rqrrrr&ryrhr$rr r r9rrr}rXrxrloggerwarningziprrrr;rrrrrrr)r4rrorrrr r"rrrqrrrr7rBrlall_hidden_statesall_self_attnsall_cross_attentionsnext_decoder_cache attn_mask mask_namer  decoder_layerrrnr next_caches r)r?zBlenderbotDecoder.forwardGsHb2C1N-TXT_T_TqTq$8$D $++JjJj "+!6IDKKst t  "#..*K!r;r?;I  &',,.s3Kde eDSC^!3A!6!>#A&#dkk*::$ {*DSEUDVW%NN,Q/03 #,DKK"8/ @ C#!m%55!}}&+jjn#&75D5P_S1VZN**t}} $ A A!**!")*&/&;IcN1E1Q(-W[% ! !.!#1*?+A7@7LYs^RV5I5U,S1[_#1&7' ! *!,M"}:KQQR'S&UU" =#3"55(4(]1-=,??(_/ @d 6  -!1 1 +4'$ '5FXlm  9+&+%1   r+r1) NNNNNNNNNNNN) rCrDrErFrrrrr3rrr?rIrJs@r)rrs[/x ?U4!" "#!!T r+rzXThe bare Blenderbot Model outputting raw hidden-states without any specific head on top.c%peZdZddgZdeffd Zedeee e jfffd Z dZ dZd Zd Zeeeee  dd eej.d eej0deej.deej.deej0deej0deej0deeeefdeeej8deej0deej8deedeedeedeedeeej8eff dZxZS)BlenderbotModeldecoder.embed_tokens.weightencoder.embed_tokens.weightr\cvt|||j|j}}|jrt j |jnd}t||j|||_ t||j|_ t||j|_ |jy)NrQr)r2r3rrrrrrrLsharedrencoderrdecoderr)r4r\rMrrNr5s r)r3zBlenderbotModel.__init__%s  "("5"5v7H7HZ 393I3Idii/s 3JP[itu (= (=  r+pretrained_model_name_or_pathc|dk(r/tjdttj|St t ||g|i|S)Nfacebook/blenderbot-90MzThe checkpoint `facebook/blenderbot-90M` is deprecated. In the future, please use the identical checkpoint `facebook/small_blenderbot-90M` with `BlenderbotSmallModel.from_pretrained('facebook/small_blenderbot-90M')` instead.)warningswarn FutureWarningrfrom_pretrainedr2r0clsr7 model_argskwargsr5s r)r=zBlenderbotModel.from_pretrained1sV (,E E MMd   (778UV V_c:;Xp[epioppr+c|jSr1)r4rs r)rz$BlenderbotModel.get_input_embeddings>s {{r+c~||_|j|j_|j|j_yr1)r4r5rr6rs r)rz$BlenderbotModel.set_input_embeddingsAs) $(KK !$(KK !r+c|jSr1)r5rs r) get_encoderzBlenderbotModel.get_encoderF ||r+c|jSr1r6rs r) get_decoderzBlenderbotModel.get_decoderIrFr+ output_typerrrordecoder_attention_maskrdecoder_head_maskr encoder_outputsr"rdecoder_inputs_embedsrrqrrrrc| | n|jj} ||n|jj}| | n|jj} ||n|jj}||j |||| | ||}nI|rGt |ts7t|dt|dkDr|dndt|dkDr|dnd}|j|||d|||| | | | || }|s||zSt|j|j|j|j|j|j|j|jS) a Returns: Example: ```python >>> from transformers import AutoTokenizer, BlenderbotModel >>> model = BlenderbotModel.from_pretrained("facebook/blenderbot-400M-distill") >>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill") >>> inputs = tokenizer("Studies have been shown that owning a dog is good for you", return_tensors="pt") >>> decoder_input_ids = tokenizer("Studies show that", return_tensors="pt").input_ids # Batch size 1 >>> outputs = model(input_ids=inputs.input_ids, decoder_input_ids=decoder_input_ids) >>> last_hidden_states = outputs.last_hidden_state >>> list(last_hidden_states.shape) [1, 6, 1280] ```N)rrorrrqrrrrrr rrorrrr r"rrrqrr)rr"decoder_hidden_statesdecoder_attentionsr#encoder_last_hidden_staterencoder_attentions)r\rqrrrr5rrrr6rrr"rlrr#)r4rrorrLrrMr rNr"rrOrrqrrdecoder_outputss r)r?zBlenderbotModel.forwardLsN2C1N-TXT_T_TqTq$8$D $++JjJj "+!6IDKK"?@Z "$u'8'8"9:Z   -Z  ((9(9:Z D>Z $D>Z 'tnZ d^!Z " uU&&');; <#Z ]HZ r+r0zRThe Blenderbot Model with a language modeling head. Can be used for summarization.c( eZdZdZdgZgdZdeffd Zede e e e jfffd ZdZd Z d&d ed e ed edej(ffd Zd edd fdZdZdZeeeeeee d'de e jBde e jDde e jBde e jBde e jDde e jDde e jDde e e#e$fde e%e jLde e jDde e jLde e jBd e ed!e ed"e ed#e ede e#e jLeff"d$Z'e(d%Z)xZ*S)("BlenderbotForConditionalGenerationrfinal_logits_bias)r1r2lm_head.weightr\cxt||t||_|j dt j d|jjjftj|j|jjjd|_ |jy)NrbrFr^)r2r3r0rregister_bufferr;zerosr4r.rrarlm_headrrs r)r3z+BlenderbotForConditionalGeneration.__init__s  $V,  0%++q$**BSBSBbBb>c2deyy1B1B1Q1QX]^  r+r7c|dk(r/tjdttj|St t ||g|i|S)Nr9zThe checkpoint `facebook/blenderbot-90M` is deprecated. In the future, please use the identical checkpoint `facebook/small_blenderbot-90M` with `BlenderbotSmallForConditionalGeneration.from_pretrained('facebook/small_blenderbot-90M')` instead.)r:r;r<rr=r2rar>s r)r=z2BlenderbotForConditionalGeneration.from_pretraineds_ (,E E MMw   ;JJKhi i7M ) ,6 :@  r+c6|jjSr1)rrErs r)rEz.BlenderbotForConditionalGeneration.get_encoderzz%%''r+c6|jjSr1)rrIrs r)rIz.BlenderbotForConditionalGeneration.get_decoderrjr+Nnew_num_tokenspad_to_multiple_of mean_resizingrrczt||||}|j|jjd|S)Nr)r2resize_token_embeddings_resize_final_logits_biasr>r$)r4rlrmrnnew_embeddingsr5s r)rpz:BlenderbotForConditionalGeneration.resize_token_embeddingss?8I[]jk &&~'<'<'B'B1'EFr+c6|jjd}||kr|jddd|f}nStjd||z f|jj}tj |j|gd}|j d|y)Nr"rrrtrb)rbr$r;rfr:rzre)r4rlold_num_tokensnew_bias extra_biass r)rqz.>s.rU_j--aZ=N=N1OPr58rrr"rreordered_past layer_pasts ` r)_reorder_cachez1BlenderbotForConditionalGeneration._reorder_cache8sT) J rcmnpopcqrrQR.! N  r+)NT)NNNNNNNNNNNNNNNN)+rCrDrEr_keys_to_ignore_on_load_missingrWrr3rXrrrYrZr[r=rErIrGrrrrprqryr|rr\rrr]rBLENDERBOT_GENERATION_EXAMPLEr;r^rSrrrr_r? staticmethodrrIrJs@r)raras ':&;#i/  HU3PRP[P[K[E\<]    ((dh!7?}\` <<<&++FG?Y5615158<=A,0487;CG=A04=A-1$(,0/3&*#L E,,-L !.L $E$4$45 L !))9)9 : L ELL) L $ELL1L 'u||4L "%(>"?@L "$u'8'8"9:L   -L  ((9(9:L ))*L D>L $D>L 'tn!L "d^#L $ uU&&'8 9%L 7ZHL \r+rac(eZdZdZfdZdZxZS)BlenderbotDecoderWrapperz This wrapper class is a helper class to correctly load pretrained checkpoints when the causal language model is used in combination with the [`EncoderDecoderModel`] framework. cDt||t||_yr1)r2r3rr6rs r)r3z!BlenderbotDecoderWrapper.__init__Ks  (0 r+c&|j|i|Sr1rH)r4argsrAs r)r?z BlenderbotDecoderWrapper.forwardOst||T,V,,r+)rCrDrErFr3r?rIrJs@r)rrEs 1-r+rc eZdZdgZfdZdZdZdZdZdZ dZ e e e  dd eej d eej"d eej$d eej$deej"deej"deeej$deej$deej deedeedeedeedeee ffdZedZxZS)BlenderbotForCausalLMrcc tj|}d|_d|_t||t ||_tj|j|jd|_ |jy)NTFr^)copydeepcopyrYis_encoder_decoderr2r3rrrra hidden_sizerrgrrs r)r3zBlenderbotForCausalLM.__init__Wsfv& $)!  -f5 yy!3!3V5F5FUS  r+cB|jjjSr1rr6rrs r)rz*BlenderbotForCausalLM.get_input_embeddingscszz!!...r+c:||jj_yr1rrs r)rz*BlenderbotForCausalLM.set_input_embeddingsfs*/ 'r+c|jSr1rxrs r)ryz+BlenderbotForCausalLM.get_output_embeddingsirFr+c||_yr1rxr{s r)r|z+BlenderbotForCausalLM.set_output_embeddingslr}r+c&||j_yr1rr6)r4r6s r) set_decoderz!BlenderbotForCausalLM.set_decoderos$ r+c.|jjSr1rrs r)rIz!BlenderbotForCausalLM.get_decoderrszz!!!r+rJrrorrrr r"rr~rrqrrrrcz| | n|jj} | | n|jj} | | n|jj} |jj ||||||||| | | |  }|j |d}d}| a| j|j} t}||jd|jj| jd}| s|f|ddz}||f|zS|St|||j|j|j|j S)a Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if the model is configured as a decoder. encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. cross_attn_head_mask (`torch.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional tensors are only required when the model is used as a decoder in a Sequence to Sequence model. Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. Indices should either be in `[0, ..., config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. Returns: Example: ```python >>> from transformers import AutoTokenizer, BlenderbotForCausalLM >>> tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill") >>> model = BlenderbotForCausalLM.from_pretrained("facebook/blenderbot-400M-distill", add_cross_attention=False) >>> assert model.config.is_decoder, f"{model.__class__} has to be configured as a decoder." >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") >>> outputs = model(**inputs) >>> logits = outputs.logits >>> expected_shape = [1, inputs.input_ids.shape[-1], model.config.vocab_size] >>> list(logits.shape) == expected_shape True ```NrQrr"r)rrr"rlrr#)r\rqrrrr6rgrr:rrhrrr"rlrr#)r4rrorrrr r"rr~rrqrrrrrrrs r)r?zBlenderbotForCausalLM.forwardusXL2C1N-TXT_T_TqTq$8$D $++JjJj &1%. s.nU_j--aZ=N=N1OPnrrrs ` r)rz$BlenderbotForCausalLM._reorder_caches=) J ncmnn N r+) NNNNNNNNNNNNN)rCrDrErWr3rrryr|rrIrrr]rr;r^rSr_rrrrr?rrrIrJs@r)rrTs*+ /0&%"+L[jk1515=A>B,07;=A59-1$(,0/3&*N E,,-N !.N  ((9(9: N !)):): ; N ELL) N 'u||4N "$u'8'8"9:N   1 12N ))*N D>N $D>N 'tnN d^N  u77 8N lN `r+r)rrar0r)DrFrrrZr:typingrrrrr;torch.utils.checkpointrtorch.nnr activationsr generationr modeling_attn_mask_utilsr r modeling_outputsrrrrrmodeling_utilsrutilsrrrrrblenderbot_smallrrconfiguration_blenderbotr get_loggerrCr$r]_CHECKPOINT_FOR_DOCrSrGr*rr-rLModulerUrrrrBLENDERBOT_START_DOCSTRINGrr\rrr0rarr__all__rr+r)rs // %!)e.]6   H %$8 %,, c [^  *2<<*$ =BLL =[B"))[B|!()<=ARYYAJuRYYup8 !DXvj 1j Z} 1} @^F /F F RXZtS)BOSSn -8 -y5yx r+